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Research Article |

SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

Author(s): Yogendra Narayan1, Meet Kumari2 and Rajeev Ranjan3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 18 October 2022

e-ISSN : 2347-470X

Page(s) : 822-825

DOI: https://doi.org/10.37391/IJEER.100410

Abstract

In the recent era of technology, biomedical signals have been attracted lots of attention regarding the development of rehabilitation robotic technology. The surface electromyography (SEMG) signals are the fabulous signals utilized in the field of robotics. In this context, SEMG signals have been acquired by twenty-five right-hand dominated healthy human subjects to discriminate the various hand gestures. The placement of SEMG electrodes has been done according to the predefined acupressure point of required hand movements. After the SEMG signal acquisition, pre-processing and noise rejection have been performed. The de-noising and four levels of SEMG signal decomposition have been accomplished by discrete wavelet transform (DWT). In this article, the third and fourth-level detail coefficients have been utilized for time-scale feature extractions. The performance of ten time-scale features has been evaluated and compared to each other with the three-fold cross-validation technique by using a Decision Tree (DT) and Linear Regression (LR) classifier. The results demonstrated that the DT classifier classification accuracy was found superior to the LR classifier. By using the DT classifier technique 96.3% accuracy has been achieved, with all combined features as a feature vector.

Keywords: DT classifier, discrete wavelet transform, LR classifier, SEMG signals




Yogendra Narayan*, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India; Email: yogendranarayan.cse@cumail.in

Meet Kumari, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India; Email: meetkumari08@yahoo.in

Rajeev Ranjan, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India; Email: rajeev.e9518@cumail.in

    [1] R. Ranjan, N. Jindal, and A. K. Singh, “Fractional S-Transform and Its Properties: A Comprehensive Survey,” Wirel. Pers. Commun., no. 0123456789, 2020, doi: 10.1007/s11277-020-07339-6.[Cross Ref]
    [2] Y. Narayan, R. M. Singh, L. Mathew, and S. Chatterji, “Surface EMG signal classification using ensemble algorithm, PCA and DWT for robot control,” in Communications in Computer and Information Science, 2018, pp. 218–230, doi: 10.1007/978-981-13-3140-4_20.[Cross Ref]
    [3] Y. Tian and J. Luo, “A new branch-and-bound approach to semi-supervised support vector machine,” Soft Comput., no. 2001, 2016, doi: 10.1007/s00500-016-2089-y.[Cross Ref]
    [4] V. Ahlawat, R. Thakur, and Y. Narayan, “support vector machine based classification improvement for EMG signal using principal component analysis,” J. Eng. Appl. Sci., vol. 13, no. 8, pp. 6341–6345, 2018.[Cross Ref]
    [5] Y. Narayan, “Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers,” Turkish J. Comput. Math. Educ., vol. 12, no. 2, pp. 3339–3344, 2021, doi: 10.17762/turcomat.v12i2.2393.[Cross Ref]
    [6] A. Phinyomark, R. Larracy, and E. Scheme, “Fractal Analysis of Human Gait Variability via Stride Interval Time Series,” Front. Physiol., vol. 11, no. April, pp. 1–12, 2020, doi: 10.3389/fphys.2020.00333.[Cross Ref]
    [7] Harendra Singh, Roop Singh, Parul Goel, Anil Singh and Naveen Sharma (2022), Automatic Framework for Vegetable Classification using Transfer-Learning. IJEER 10(2), 405-410. DOI: 10.37391/IJEER.100257. [Cross Ref]
    [8] Manoj Kumar, Dr Pratiksha Gautam and Dr Vijay Bhaskar (2022), Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders. IJEER 10(2), 117-121. DOI: 10.37391/IJEER.100211. [Cross Ref]
    [9] Y. Narayan, “Direct comparison of SVM and LR classifier for SEMG signal classification using TFD features,” Mater. Today Proc., vol. 45, pp. 3543–3546, 2021, doi: 10.1016/j.matpr.2020.12.979.[Cross Ref]
    [10] Y. Narayan, “Analysis of MLP and DSLVQ Classifiers for EEG Signals Based Movements Identification,” Oct. 2021, doi: 10.1109/GCAT52182.2021.9587868.[Cross Ref]
    [11] V. Ahlawat, Y. Narayan, and D. Kumar, “DWT-Based Hand Movement Identification of EMG Signals Using SVM,” Lect. Notes Networks Syst., vol. 192 LNNS, pp. 495–505, 2021, doi: 10.1007/978-981-33-6546-9_47. [Cross Ref]
    [12] Y. Narayan, “SEMG signal classification using KNN classifier with FD and TFD features,” Mater. Today Proc., vol. 37, no. Part 2, pp. 3219–3225, 2020, doi: 10.1016/j.matpr.2020.09.089.[Cross Ref]
    [13] Y. Narayan, “Eeg signals classification using svm and rf classifier for left and right-hand movement,” J. Green Eng., vol. 10, no. 11, pp. 10691–10701, 2020[Cross Ref]
    [14] M. Wang, J. Hu, and H. A. Abbass, “BrainPrint.: EEG Biometric Identification based on Analyzing Brain Connectivity Graphs,” Pattern Recognit., vol. 300, no. 5, p. 107381, 2020, doi: 10.1016/j.patcog.2020.107381.[Cross Ref]
    [15] Y. Narayan, V. Ahlawat, and S. Kumar, “Pattern recognition of sEMG signals using DWT based feature and SVM Classifier,” Int. J. Adv. Sci. Technol., vol. 29, no. 10, pp. 2243–2256, 2020.[Cross Ref]
    [16] Y. Narayan, “Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification,” Mater. Today Proc., vol. 37, no. Part 2, pp. 3241–3245, 2020, doi: 10.1016/j.matpr.2020.09.093.[Cross Ref]
    [17] Manjinder Singh, Harpreet Kaur (2016), Enhanced Image Inpainting in Remotely Sensed Images by Optimizing NLTV model by Ant Colony Optimization. IJEER 4(3), 91-97. DOI: 10.37391/IJEER.040306. https://ijeer.forexjournal.co.in/papers-pdf/ijeer-040306.pdf[Cross Ref]
    [18] N. Rabin, M. Kahlon, S. Malayev, and A. Ratnovsky, “Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques,” Expert Syst. Appl., vol. 149, p. 113281, 2020, doi: 10.1016/j.eswa.2020.113281.[Cross Ref]
    [19] T. Tuncer, S. Dogan, and A. Subasi, “Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition,” Biomed. Signal Process. Control, vol. 58, p. 101872, 2020, doi: 10.1016/j.bspc.2020.101872.[Cross Ref]
    [20] Y. Narayan, L. Mathew, and S. Chatterji, “sEMG signal classification using Discrete Wavelet Transform and Decision Tree classifier,” Int. J. Control Theory Appl., vol. 10, no. 6, pp. 511–517, 2017.[Cross Ref]
    [21] Y. Narayan, L. Mathew, and S. Chatterji, “SEMG signal classification with novel feature extraction using different machine learning approaches,” J. Intell. Fuzzy Syst., vol. 35, no. 5, pp. 5099–5109, 2018, doi: 10.3233/JIFS-169794.[Cross Ref]
    [22] J. Too, A. R. Abdullah, N. M. Saad, and N. M. Ali, “Feature selection based on binary tree growth algorithm for the classification of myoelectric signals,” Machines, vol. 6, no. 4, 2018, doi: 10.3390/machines6040065.[Cross Ref]
    [23] Narayan, Y., & Ranjan, R. (2022). EEG Signals Classification for Right-and Left-Hand Movement Discrimination Using SVM and LDA Classifiers. In High Performance Computing and Networking (pp. 133-142). Springer, Singapore.[Cross Ref]
    [24] Sarabpreet Kaur, Jyoti Patel (2018), A Robust Image Mosaicing Technique Using Frequency Domain. IJEER 6 (1), 1-8. DOI: 10.37391/IJEER.060101. https://ijeer.forexjournal.co.in/archive/volume-6/ijeer-060101.php[Cross Ref]

Yogendra Narayan, Meet Kumari and Rajeev Ranjan (2022), SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features. IJEER 10(4), 822-825. DOI: 10.37391/IJEER.100410.